Assessment of spinal column integrity

ABSTRACT

A method of assessing spinal column stability involves receiving image data corresponding to a spinal column of a patient; determining, based on the image data, a material strength of bony anatomy in at least a portion of the spinal column; completing a first stability assessment of the spinal column, based at least in part on the determined material strength; modifying the image data to simulate removal of bony anatomy or soft tissue from the spinal column to yield modified image data; and completing a second stability assessment of the spinal column, based at least in part on the determined material strength and the modified image data.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.16/845,788, filed on Apr. 10, 2020, and entitled “ASSESSMENT OF SPINALINTEGRITY,” which application is incorporated herein by reference in itsentirety.

FIELD

The present technology is related generally to decompression treatmentand, more particularly, to evaluation of the effect of decompressiontreatment on spinal column integrity.

BACKGROUND

Lumbar spinal stenosis, or compression of neural elements due to anarrowing of the spinal canal, is one of the largest contributors tospinal procedures in patients over 65. Spinal decompression proceduresfor relieving a spinal compression are delicate, time consuming, andhigh-risk tasks. Diagnosis of spinal stenosis is typically accomplishedusing a combination of patient imaging, patient verbal inputs, patientreported function and physical examination of the patient. Oncediagnosed, spinal stenosis may be treated with non-invasive means suchas massages, chiropractic treatments, and/or acupuncture, and/or withinvasive procedures including laminectomy, laminotomy, discectomy, andso forth.

Access to and removal of bony anatomy and/or soft tissue from the spinalcolumn can affect the integrity of the spinal column, including thestability and/or mobility of the spinal column. Iatrogenic instabilityis instability of the spinal column resulting from surgical or medicalintervention and can negatively affect a patient's quality of life.

Conventional methods for assessing spinal column integrity (includingthe risk that a given procedure will cause iatrogenic instability) relyon a surgeon's prior experience, knowledge, and judgment. Suchconventional methods are time consuming, subjective, complex, and maynot be recorded for future reference or use. Whether spinal fusion orother methods of improving spinal column integrity are needed as aresult of decompression or other spinal surgery is a subjectivedetermination.

SUMMARY

Embodiments of the present disclosure advantageously provide objectiveapproaches to assessing the effect of a decompression procedure or otherspinal surgery on spinal stability, including in particular forassessing the risk that a given decompression or other spinal surgerywill result in iatrogenic instability. Embodiments of the presentdisclosure thus beneficially augment the treating physician's priorexperience, knowledge, and judgment with objective data. Embodiments ofthe present disclosure may also beneficially enable a treating physicianto assess the effect of one or more possible decompressions or otherspinal surgery procedures on spinal column integrity, select a procedurewith a least negative impact on spinal column integrity, and/or preparefor a spinal fusion or other stability-enhancing procedure in advance ofa surgical procedure that is expected to negatively affect spinal columnintegrity.

A method of assessing spinal column stability according to oneembodiment of the present disclosure comprises: receiving image datacorresponding to a spinal column of a patient; determining, based on theimage data, a material strength of bony anatomy in at least a portion ofthe spinal column; completing a first stability assessment of the spinalcolumn, based at least in part on the determined material strength;modifying the image data to simulate removal of bony anatomy or softtissue from the spinal column to yield modified image data; andcompleting a second stability assessment of the spinal column, based atleast in part on the determined material strength and the modified imagedata.

The method may further comprise comparing the second stabilityassessment to the first stability assessment and causing informationcorresponding to the comparison to be displayed via a user interface.The method may further comprise comparing the second stabilityassessment to a predetermined threshold and causing informationcorresponding to the comparison to be displayed via a user interface.The information corresponding to the comparison may be an indicationthat a risk of instability is high, medium, or low. The image data maycorrespond to a 3D image of the spinal column, the 3D image comprising aplurality of slices, and the determining may comprise determining amaterial strength of bony anatomy in each of the plurality of slices.

The simulated removal of bony anatomy or soft tissue from the spinalcolumn may correspond to a received user selection of one of alaminectomy, a laminotomy, or a foraminotomy. The simulated removal ofbony anatomy or soft tissue from the spinal column may correspond bothto removal of first bony anatomy or soft tissue from the spinal columnto correct stenosis, and to removal of second bony anatomy or softtissue from the spinal column to enable access to the first bony anatomyor soft tissue.

The image data may correspond to a plurality of 2D images of the spinalcolumn. The image data may correspond to a CT scan, and determining thematerial strength of bony anatomy in at least the portion of the spinalcolumn may be based on a measurement in Hounsfield units of the bonyanatomy.

A method of assessing spinal column stability according to anotherembodiment of the present disclosure comprises: receiving image datacorresponding to a spinal column of a patient; receiving mobility datacorresponding to an initial mobility assessment of the spinal column;modifying, based on a user input, the image data to simulate removal ofbony anatomy or soft tissue from the spinal column to yield modifiedimage data; and generating an updated mobility assessment of the spinalcolumn, based on the modified image data.

The method may further comprise automatically analyzing the image datato identify stenosis in the spinal column. The automatic analysis may bebased on a comparison of image data corresponding to a first portion ofthe spinal column to image data corresponding to a second portion of thespinal column, the second portion different than the first portion. Theautomatic analysis may utilize a predefined algorithm. The simulatedremoval of bony anatomy or soft tissue from the spinal column may bebased on the identified stenosis. The user input may correspond to auser selection of one of a laminectomy, a laminotomy, or a foraminotomy.The simulated removal of bony anatomy or soft tissue from the spinalcolumn may correspond both to removal of first bony anatomy or softtissue from the spinal column to correct stenosis, and to removal ofsecond bony anatomy or soft tissue from the spinal column to enableaccess to the first bony anatomy or soft tissue.

A system for assessing spinal column stability according to yet anotherembodiment of the present disclosure comprises: a communicationinterface; a processor; and a memory. The memory stores instructions forexecution by the processor that, when executed, cause the processor to:receive preoperative image data corresponding to a spinal column of apatient in a first state; identify spinal stenosis based on thepreoperative image data; determine a portion of bony anatomy or softtissue to remove to correct the spinal stenosis; simulate removal of theportion of bony anatomy or soft tissue to yield modified preoperativeimage data; and generate a first stability assessment of the spinalcolumn based on the modified preoperative image data.

The memory may store additional instructions that, when executed,further cause the processor to: receive postoperative image datacorresponding to the spinal column of the patient, the postoperativeimage data reflecting removal of some bony anatomy or soft tissuerelative to the preoperative image data; generate a second stabilityassessment of the spinal column based on the postoperative image data;generate a decompression surgical plan to correct the spinal stenosis;cause the decompression surgical plan to be displayed on a userinterface; and/or determine a spinal column level of the spinalstenosis.

The details of one or more aspects of the disclosure are set forth inthe accompanying drawings and the description below. Other features,objects, and advantages of the techniques described in this disclosurewill be apparent from the description and drawings, and from the claims.

The phrases “at least one”, “one or more”, and “and/or” are open-endedexpressions that are both conjunctive and disjunctive in operation. Forexample, each of the expressions “at least one of A, B and C”, “at leastone of A, B, or C”, “one or more of A, B, and C”, “one or more of A, B,or C” and “A, B, and/or C” means A alone, B alone, C alone, A and Btogether, A and C together, B and C together, or A, B and C together.When each one of A, B, and C in the above expressions refers to anelement, such as X, Y, and Z, or class of elements, such as X₁-X_(n),Y₁-Y_(m), and Z₁-Z_(o), the phrase is intended to refer to a singleelement selected from X, Y, and Z, a combination of elements selectedfrom the same class (e.g., X₁ and X₂) as well as a combination ofelements selected from two or more classes (e.g., Y₁ and Z_(o)).

The term “a” or “an” entity refers to one or more of that entity. Assuch, the terms “a” (or “an”), “one or more” and “at least one” can beused interchangeably herein. It is also to be noted that the terms“comprising”, “including”, and “having” can be used interchangeably.

The preceding is a simplified summary of the disclosure to provide anunderstanding of some aspects of the disclosure. This summary is neitheran extensive nor exhaustive overview of the disclosure and its variousaspects, embodiments, and configurations. It is intended neither toidentify key or critical elements of the disclosure nor to delineate thescope of the disclosure but to present selected concepts of thedisclosure in a simplified form as an introduction to the more detaileddescription presented below. As will be appreciated, other aspects,embodiments, and configurations of the disclosure are possibleutilizing, alone or in combination, one or more of the features setforth above or described in detail below.

Numerous additional features and advantages of the present inventionwill become apparent to those skilled in the art upon consideration ofthe embodiment descriptions provided hereinbelow.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings are incorporated into and form a part of thespecification to illustrate several examples of the present disclosure.These drawings, together with the description, explain the principles ofthe disclosure. The drawings simply illustrate preferred and alternativeexamples of how the disclosure can be made and used and are not to beconstrued as limiting the disclosure to only the illustrated anddescribed examples. Further features and advantages will become apparentfrom the following, more detailed, description of the various aspects,embodiments, and configurations of the disclosure, as illustrated by thedrawings referenced below.

FIG. 1 is a block diagram of a system according to at least oneembodiment of the present disclosure;

FIG. 2 is a flowchart of a method according to at least one embodimentof the present disclosure;

FIG. 3A is a lateral image of a spine region according to at least oneembodiment of the present disclosure;

FIG. 3B is a superior image of a vertebra within the spine region ofFIG. 3A, according to at least one embodiment of the present disclosure;

FIG. 4A is another lateral image of a spine region according to at leastone embodiment of the present disclosure;

FIG. 4B is a superior image of a vertebra within the spine region ofFIG. 4A, according to at least one embodiment of the present disclosure;

FIG. 5A is a lateral image of a spine region according to at least oneembodiment of the present disclosure;

FIG. 5B is a superior image of a vertebra within the spine region ofFIG. 4A, according to at least one embodiment of the present disclosure;

FIG. 6 is another flowchart of a method according to at least oneembodiment of the present disclosure; and

FIG. 7 is another flowchart of a method according to at least oneembodiment of the present disclosure.

DETAILED DESCRIPTION

It should be understood that various aspects disclosed herein may becombined in different combinations than the combinations specificallypresented in the description and accompanying drawings. It should alsobe understood that, depending on the example or embodiment, certain actsor events of any of the processes or methods described herein may beperformed in a different sequence, may be added, merged, or left outaltogether (e.g., all described acts or events may not be necessary tocarry out the techniques). In addition, while certain aspects of thisdisclosure are described as being performed by a single module or unitfor purposes of clarity, it should be understood that the techniques ofthis disclosure may be performed by a combination of units or modulesassociated with, for example, a computing device and/or a medicaldevice.

In one or more examples, the described methods, processes, andtechniques may be implemented in hardware, software, firmware, or anycombination thereof. If implemented in software, the functions may bestored as one or more instructions or code on a computer-readable mediumand executed by a hardware-based processing unit. Computer-readablemedia may include non-transitory computer-readable media, whichcorresponds to a tangible medium such as data storage media (e.g., RAM,ROM, EEPROM, flash memory, or any other medium that can be used to storedesired program code in the form of instructions or data structures andthat can be accessed by a computer).

Instructions may be executed by one or more processors, such as one ormore digital signal processors (DSPs), general purpose microprocessors(e.g., Intel Core i3, i5, i7, or i9 processors; Intel Celeronprocessors; Intel Xeon processors; Intel Pentium processors; AMD Ryzenprocessors; AMD Athlon processors; AMD Phenom processors; Apple A10 or10X Fusion processors; Apple A11, A12, A12X, A12Z, or A13 Bionicprocessors; or any other general purpose microprocessors), applicationspecific integrated circuits (ASICs), field programmable logic arrays(FPGAs), or other equivalent integrated or discrete logic circuitry.Accordingly, the term “processor” as used herein may refer to any of theforegoing structure or any other physical structure suitable forimplementation of the described techniques. Also, the techniques couldbe fully implemented in one or more circuits or logic elements.

Before any embodiments of the disclosure are explained in detail, it isto be understood that the disclosure is not limited in its applicationto the details of construction and the arrangement of components setforth in the following description or illustrated in the drawings. Thedisclosure is capable of other embodiments and of being practiced or ofbeing carried out in various ways. Also, it is to be understood that thephraseology and terminology used herein is for the purpose ofdescription and should not be regarded as limiting. The use of“including,” “comprising,” or “having” and variations thereof herein ismeant to encompass the items listed thereafter and equivalents thereofas well as additional items. Further, the present disclosure may useexamples to illustrate one or more aspects thereof. Unless explicitlystated otherwise, the use or listing of one or more examples (which maybe denoted by “for example,” “by way of example,” “e.g.,” “such as,” orsimilar language) is not intended to and does not limit the scope of thepresent disclosure.

Turning first to FIG. 1, a block diagram of a system 100 according to atleast one embodiment of the present disclosure is shown. The system 100may be used to process image data, detect spinal stenosis, carry out oneor more virtual simulations, assess spinal column integrity, generate adecompression plan, generate a fusion plan, and/or carry out otheraspects of one or more of the methods disclosed herein. The system 100comprises a computing device 102, an imaging device 112, a database 114,and/or a cloud or other network 116. The computing device 102 comprisesa processor 104, a memory 106, a communication interface 108, and a userinterface 110. Systems such as the system 100 according to otherembodiments of the present disclosure may comprise more or fewercomponents than the system 100.

The processor 104 of the computing device 102 may be any processordescribed herein or any similar processor. The processor may beconfigured to execute instructions stored in the memory 106, whichinstructions may cause the processor to carry out one or more computingsteps utilized or based on data received from the imaging device 112,the database 114, and/or the cloud 116.

The memory 106 may be or comprise RAM, DRAM, SDRAM, other solid-statememory, any memory described herein, or any other non-transitory memoryfor storing computer-readable data and/or instructions. The memory 106may store information or data useful for completing any step of any ofthe methods 200, 600, 600 and/or 800 described herein. The memory maystore, for example, image processing instructions 118, stenosisdetection instructions 120, simulation instructions 122, and/orstability assessment instructions 124. Such instructions may, in someembodiments, be organized into one or more applications, modules,packages, layers, or engines. The instructions may cause the processor104 to manipulate data stored in the memory 106 and/or received from theimaging device 112, the database 114, and/or the cloud 116.

The computing device 102 may also comprise a communication interface108. The communication interface 108 may be used for receiving imagedata or other information from an external source (such as the imagingdevice 112, the database 114, and/or the cloud 116), and/or fortransmitting simulation results, decompression plans, fusion plans,images, or other information to an external source (e.g., the database114, the cloud 116, another computing device 102). The communicationinterface 108 may comprise one or more wired interfaces (e.g., a USBport, an ethernet port, a Firewire port) and/or one or more wirelessinterfaces (configured, for example, to transmit information via one ormore wireless communication protocols such as 802.11a/b/g/n, Bluetooth,NFC, ZigBee, and so forth). In some embodiments, the communicationinterface may be useful for enabling the device 102 to communicate withone or more other processors 104 or computing devices 102, whether toreduce the time needed to accomplish a computing-intensive task or forany other reason.

The computing device 102 may also comprise one or more user interfaces110. The user interface 110 may be or comprise a keyboard, mouse,trackball, monitor, television, touchscreen, and/or any other device forreceiving information from a user and/or for providing information to auser. The user interface 110 may be used, for example, to receive a userselection or other user input regarding a decompression procedure tosimulate and/or plan; to receive a user selection or other user inputregarding a type of approach to use to executed the decompressionprocedure; to receive user input regarding a portion of bony anatomyand/or soft tissue to remove to achieve decompression; to display aproposed decompression plan to a surgeon or other user; to displaysimulation results to a surgeon or other user; to display informationcorresponding to a stability assessment, a mobility assessment, oranother spinal integrity assessment to a surgeon or other user; todisplay information about a risk of iatrogenic instability to a surgeonor other user; to display a decompression plan to a surgeon or otheruser; and/or to display a fusion plan to a surgeon or other user. Insome embodiments, the user interface 110 may be useful to allow asurgeon or other user to modify a decompression plan, a fusion plan, orother displayed information.

Although the user interface 110 is shown as part of the computing device102, in some embodiments, the computing device 102 may utilize a userinterface 110 that is housed separately from one or more remainingcomponents of the computing device 102. In some embodiments, the userinterface 110 may be located proximate one or more other components ofthe computing device 102, while in other embodiments, the user interface110 may be located remotely from one or more other components of thecomputer device 102.

The imaging device 112 is operable to image an anatomy of a patient(e.g., a spine region) to yield image data (e.g., image data depicting aspinal column of a patient. The image data may correspond to the entirespinal column of the patient or to a portion of the spinal column of thepatient. The imaging device 802 may be, but is not limited to, amagnetic resonance imaging (MRI) scanner, a CT scanner or other X-raymachine, an ultrasound scanner, an optical computed tomography scanner,or any other imaging device suitable for obtaining images of a spinalcolumn of a patient.

The database 114 may store one or more images taken by one or moreimaging devices 112 and may be configured to provide one or more suchimages (electronically, in the form of image data) to computing devicesuch as the computing device 102. The database 114 may be configured toprovide image data to a computing device 102 directly (e.g., when thecomputing device 102 and the database 114 are co-located, and/or areconnected to the same local area network) and/or via the cloud 116(e.g., when the computing device 102 and the database 114 are notco-located or otherwise connected to the same local area network). Insome embodiments, the database 114 may be or comprise part of a hospitalimage storage system, such as a picture archiving and communicationsystem (PACS), a health information system (HIS), and/or another systemfor collecting, storing, managing, and/or transmitting electronicmedical records including image data.

The cloud 116 may be or represent the Internet or any other wide areanetwork. The computing device 108 may be connected to the cloud 116through the communication interface 108, via a wired or wirelessconnection. In some embodiments, the computing device 102 maycommunicate with the imaging device 112, the database 114, one or moreother computing device 102, and/or one or more other components of acomputing device 102 (e.g., a display or other user interface 110) viathe cloud 116.

Turning now to FIGS. 2 through 5B, a method 200 according to embodimentsof the present disclosure may be executed in whole or in part on acomputing device 102.

The method 200 comprises receiving and processing preoperative imagedata (step 202). The preoperative image data may comprise or correspondto, for example, a three-dimensional image of a spinal column of apatient, and may comprise data corresponding to a plurality ofindividual cuts, slices, or sections of the spinal column of the patientthat together make up the three-dimensional image of the spinal column.Additionally or alternatively, the preoperative image data may compriseor correspond to one or more two-dimensional images of the spinal columnof the patient. For example, the preoperative image data may correspondto images such as the image 300 of FIG. 3A, and/or the image 302 of FIG.3B. Where the image data comprises or corresponds to a plurality oftwo-dimensional images of the spinal column of the patient, theplurality of two-dimensional images may be sufficient to construct orreconstruct a three-dimensional image or model of the spinal column. Thepreoperative image data may correspond to a preoperative image taken ofthe spinal column of the patient using an imaging device 112, such as anMRI scanner, a CT scanner, or another imaging device. The preoperativeimage data may contain data for an entire spinal column of the patientor for a portion of the spinal column of the patient. The preoperativeimage data may be received from an imaging device 112, a database 114,the cloud 114, or any other source, and may be received via thecommunication interface 108.

Processing of the preoperative image data may include applying one ormore filters to the image data to prepare the image data for furtherprocessing. Processing of the preoperative image data may also includesegmenting the preoperative image data to identify, for example, one ormore vertebrae 310, one or more discs 312, a spinal cord 304, and/or oneor more nerve exits 306 represented by the image data. The segmentingmay utilize feature identification, machine learning, or any othersegmentation method. Additionally or alternatively, the processing mayinclude measuring, inferring, calculating, or otherwise identifying oneor more properties of the bony anatomy or soft tissue represented in theimage data. For example, the processing may include determining ameasurement in Hounsfield units of one or more vertebrae 310 or portionsthereof and converting the measurement into a bone mineral density valueor other value representative of the strength or stiffness of the bonyanatomy in question. Any other measurement or algorithm may be utilizedto measure, infer, calculate, or otherwise determine a strength orstiffness of one or more anatomical elements represented in the imagedata. In some embodiments, the processing yields a material strength orstiffness determination for a plurality of vertebra 310 represented inthe image data, or for every vertebra 310 represented in the image data.Also in some embodiments, the in silico modeling and processing yields amaterial strength or stiffness determination for a plurality of discs312 represented in the image data, or for every disc represented in theimage data.

The method 200 also comprises identifying spinal stenosis based on thepreoperative image data (step 204). The stenosis may be identified inthe central canal and/or in a lateral recess of the spinal columnrepresented by the image data. In some embodiments, the stenosis may beidentified by comparing one or more attributes of the spinal cord 304 atone location of the spinal column represented in the image data with thecorresponding one or more attributes of the spinal cord 304 at anotherlocation of the spinal column represented in the image data. Forexample, the stenosis may be identified by comparing a diameter of thespinal cord at a first level of the spinal column with a diameter of thespinal cord at one or more other levels of the spinal column, which oneor more other levels may be adjacent the first level. A sudden or rapidchange in the diameter of the spinal column between or among adjacent orproximate levels may indicate stenosis.

In other embodiments, the stenosis may be identified by applying apredetermined algorithm to the image data. The algorithm may be or havebeen generated, for example, by a machine learning engine based ontraining data.

In still other embodiments, a surgeon or other user may identify thestenosis by providing one or more inputs via a user interface 110. Insuch embodiments, the identification of the stenosis may be based on theimage data and/or additional information obtained by otherwise known tothe surgeon or other user, such as information provided by the patientor uncovered during a neurologic exam.

The method 200 further comprises determining one or more symptomaticlevels of the spinal stenosis (step 206). In some embodiments, theidentified stenosis may be located at only one level of the spinalcolumn represented in the image data. In other embodiments, theidentified stenosis may be located at a plurality of levels of thespinal column. Based on the processing of the image data in step 202,and/or the identification of the stenosis in step 204, the level(s) atwhich the stenosis exists may be determined and, in some embodiments,recorded (e.g., in a memory 106) and/or reported (e.g., via a userinterface 110). The level(s) at which the stenosis exists is/are thetarget level(s) for purposes of steps 208 and 210.

The method 200 also comprises identifying bony anatomy and/or softtissue to be removed at the target level(s) (step 208). Correction ofspinal stenosis often involves decompression, or removal of bony anatomyand/or soft tissue that is compressing the spinal cord. In someembodiments, an algorithm is used to identify bony anatomy and/or softtissue at the target level(s) that needs to be removed to create enoughspace for the spinal cord 304 to return to a normal diameter (orotherwise be freed from compression). For example, in FIGS. 4A-4B, themarkings 402, 404, and 406 identify bony anatomy and/or soft tissue thatneeds to be removed to free the spinal cord 304 of compression. The bonyanatomy may be, for example, a portion of one or more vertebrae 310, andthe soft tissue may be, for example, all or part of a disc 312.

In some embodiments, the identifying of bony anatomy and/or soft tissueto be removed at the target level(s) may comprise receiving user input(e.g., from a surgeon or other user) via a user interface 110 thatidentifies the bony anatomy and/or soft tissue to be removed. Forexample, the user input may comprise a user selection of one of alaminotomy, laminectomy, foraminotomy, discectomy, or other procedure.Based on the user selection, a recommended portion of bony anatomyand/or soft tissue may be automatically identified for removal, or thesurgeon or other user may identify the portion of bony anatomy and/orsoft tissue to be removed. For example, if the user input is alaminectomy, a lamina of a vertebra 310 proximate the identifiedstenosis may be automatically identified for removal, or the user mayselect a lamina of a vertebra 310 for removal.

Often, the bony anatomy or soft tissue that needs to be removed in adecompression procedure is not readily accessible to a surgeon. As aresult, the surgeon must remove additional bony anatomy and/or softtissue from the spinal column simply to access the bony anatomy and/orsoft tissue causing the stenosis. In some embodiments, then,identification of the bony anatomy and/or soft tissue to be removedincludes not only identifying the bony anatomy and/or soft tissue thatneeds to be removed to correct the stenosis, but also identifying theoptimal trajectory and/or bony anatomy and/or soft tissue that needs tobe removed to access the stenosis-causing bony anatomy and/or softtissue. The bony anatomy and/or soft tissue that must be removed toaccess the stenosis-causing bony anatomy and/or soft tissue may beidentified automatically (e.g., by applying an algorithm selected basedon the procedure to be completed and/or predefined by a machine learningengine or otherwise) or via additional user input via a user interface110.

The identifying step 208 may further comprise marking the identifiedbony anatomy and/or soft tissue in the image data.

The method 200 further comprises virtually removing the bony anatomyand/or soft tissue at the target level(s) in a simulated decompressionprocedure, to yield modified preoperative image data (step 210). Thevirtual removal of the bony anatomy and/or soft tissue may comprisemodifying the image data to substitute a virtual material having littleor no strength or stiffness for the identified bony anatomy and/or softtissue. Alternatively, the virtual removal of the bony anatomy and/orsoft tissue may comprise simply deleting the portion of the image datarepresenting the bony anatomy and/or the soft tissue to be removed. Asanother alternative, the virtual removal of the bony anatomy and/or softtissue may comprise assigning a Hounsfield units measurement of zero tothe portion of the image data representing the bony anatomy and/or thesoft tissue to be removed. FIGS. 5A and 5B show removed portions 502,504, and 506 of bony anatomy and/or soft tissue from an imaged spinalcolumn.

The method 200 also comprises assessing a stability of the spinal columnbased on the modified preoperative image data (step 212). Assessing thestability of the spinal column may comprise, for example, running avirtual 6-degrees of motion analysis based on the modified preoperativeimage data to assess the predicted stability of the spine following theplanned procedure. The analysis may evaluate the stresses that will bepresent in one or more vertebrae or section of vertebra of the spinalcolumn represented in the modified preoperative image data while thespinal column is in a neutral position and/or while the spinal column isin one or more positions of flexion. In some embodiments, a virtual6-degree of motion analysis may be conducted using the originalpreoperative image data, the results of which may be used as a referencefor the virtual 6-degree of motion analysis conducted using the modifiedpreoperative image data to assess how the planned procedure might affectthe stability of the spine relative to its preoperative level ofstability. In some embodiments, assessing the stability of the spinalcolumn may be the same as or similar to conducting a finite elementanalysis on the spinal column (as it is represented in the modifiedpreoperative image data).

The result of the assessment (whether conducting using a virtual6-degrees of motion analysis or otherwise) may be a calculated level ofpredicted stability or instability of the spinal column (measured, forexample, in millimeters of translation or angulation, in N/m², orotherwise); an indication of the predicted maximum stress that one ormore vertebrae of the spinal column will experience; an indication thata predicted level of stress for one or more vertebrae of the spinalcolumn exceeds or approaches a predetermined threshold; or any otherindication relating to the predicted stability or instability of thespinal column represented in the modified preoperative image data if theplanned procedure is carried out. In some embodiments, the result of thevirtual 6-degrees of motion analysis may be an indication that acalculated risk of iatrogenic instability is high, medium, or low, whichindication may be based on comparing a calculated or otherwise predictediatrogenic instability to one or more predetermined thresholds.

The spinal column stability assessment of step 212 may also evaluatewhether and/or how removal of bony anatomy and/or soft tissue from thespinal column during the planned procedure affects movement of thespine. For example, removal of bony anatomy and/or soft tissue from thespinal column may result in at least a portion of the spinal columnbeing less constrained and able to move more freely. This, in turn, mayaffect the stresses imposed on one or more elements of the spinalcolumn.

The method 200 further comprises determining a decompression plan andpossibly an implant placement plan (step 214). The decompression planmay be a plan for carrying out the simulated procedure used to modifythe preoperative data prior to conducting spinal column stabilityassessment in step 212. The decompression plan may include informationabout which bony anatomy and/or soft tissue to remove to correct anidentified stenosis. The decompression plan may also include informationabout which bony anatomy and/or soft tissue to remove to gain access tothe bony anatomy and/or soft tissue is causing the stenotic condition.The decompression plan may include an identification of whichinstruments or types of instruments to use for one or more steps of theplanned decompression, the trajectory for the decompression, and/or theorder of steps to carry out the planned decompression. The decompressionplan may be generated automatically and then presented to a surgeon orother user for review, modification, and/or approval. Alternatively, thedecompression plan may be generated through a combination ofautomatically generated recommendations and user input regarding, forexample, a desired decompression procedure, a desired approach forcarrying out the decompression procedure, and/or which portion orportions of bony anatomy and/or soft tissue to remove to correct theidentified stenosis. As yet another alternative, the decompression planmay be generated based solely on user input.

The decompression plan may be a plan intended for execution manually,e.g., by a surgeon utilizing hand-operated tools. The decompression planmay alternatively be a plan intended for execution by a surgical robot,or with the assistance of a surgical robot.

In some embodiments, the step 214 may also comprise determining a fusionplan, based on the results of the assessment in step 212. For example,if the assessment determines that the planned decompression procedurewill have little or no effect on the stability of the spinal column orthe stresses that will be imposed on the vertebrae of the spinal column,and/or if the assessment determines that the risk of iatrogenicinstability is low, then no fusion plan may be prepared. Alternatively,if the assessment determines that the planned decompression procedurewill have a material effect on the stability of the spinal column or thestresses that will be imposed on the vertebrae of the spinal column,and/or if the assessment determines that the risk of iatrogenicinstability is high, then a fusion plan may be prepared.

The fusion plan may be a plan for improving the predicted postoperativestability of the spinal column, whether through the use of a bone graftand/or a spinal implant (whether metal plates to secure two or morevertebrae together, or a rod secured to pedicle screws that are insertedin the vertebrae to be fused, or an artificial disc, or anintervertebral disc, or otherwise). As with the decompression plan, thefusion plan may be generated automatically and then presented to asurgeon or other user for review, modification, and/or approval.Alternatively, the fusion plan may be generated through a combination ofautomatically generated recommendations and user input. As yet anotheralternative, the fusion plan may be generated based solely on userinput.

The method 200 also comprises causing a decompression plan to bedisplayed on a monitor or other user interface in an operating room(step 216). The decompression plan may be displayed on a monitor,overlaid on a lens using augmented reality, or other user interface inthe operating room to facilitate execution of the decompression plan bya surgeon. The displayed decompression plan may be augmented withsurgical navigation and/or other systems to assist the surgeon inremoving the correct bony anatomy and/or soft tissue from the spinalcolumn of the patient to correct the stenosis, and/or to gain access tothe bony anatomy and/or soft tissue that is causing the stenosis. Insome embodiments, the displayed decompression plan will be carried outmanually, while in other embodiments, the displayed decompression planwill be carried out automatically (e.g., by a surgical robot). In stillother embodiments, the displayed decompression plan may be carried outwith robotic assistance.

The method 200 further comprises receiving and processing postoperativeimage data (step 218). The received postoperative image data may beobtained from an imaging device 112 in the operating room or elsewherethat is used to image the spinal column of the patient following thedecompression procedure, and/or from a database 114 in which such animage has been stored, and/or via the cloud 116. The receivedpostoperative image data may be processed in any of the same or similarways described above with respect to processing of the image data instep 202. The receiving and processing of the postoperative image datamay occur while the patient is still in the operating room and may occuras soon as the bony anatomy and/or soft tissue causing the identifiedstenosis has been removed.

The method 200 also comprises assessing a stability of the spinal columnbased on the postoperative image data (step 220). The assessing may becompleted in any of the same or similar ways described above withrespect to assessing a stability of the spinal column based on themodified postoperative image data in step 212. Additionally, the resultsof the assessment may be the same or similar types of results asdescribed above in connection with step 212. The assessing may occurwhile the patient is still undergoing surgery.

The method 200 further comprises causing a surgical plan to be displayedon a monitor or other user interface in an operating room (step 222).Where the results of the assessment in step 220 indicate that the riskof iatrogenic instability is high, or otherwise indicate that thedecompression procedure has compromised spinal integrity in a way thatnecessitates surgical implant placement or other corrective measures,the method 200 may display to a surgeon in the operating room anoperative or surgical plan to facilitate execution of the plan by thesurgeon. The displayed operative plan may be a surgical plan that wasprepared in step 214, or an operative plan that is prepared based on theresults of the assessment in step 220. In the former instance, thesurgical plan may be modified by the surgeon or another user based onthe results of the assessment in step 220. In the latter instance, theoperative plan may be generated automatically, prepared with acombination of automated recommendations and user input, and/or preparedsolely based on user input.

The method 200 beneficially allows a surgeon or other physician torespond in real time or near real time to a determined risk ofiatrogenic instability resulting from a decompression procedure. Themethod 200 thus beneficially avoids situations in which a patientundergoes a first decompression operation, and then must return to theoperating room and/or hospital to undergo a second fusion operation. Themethod 200 also beneficially provides an objective measure of a risk ofiatrogenic instability, thus helping to relieve treating physicians ofthe burden of making a subjective determination as to whether fusion isneeded (whether at the time of or immediately after a decompressionprocedure, or based on a subsequent diagnosis of iatrogenicinstability).

Although described with respect to a correcting compression of a spinalcord 304, the method 200 may also be used in connection with correctingcompression of a traversing or exiting nerve 306.

Turning now to FIG. 6, a method 600 of assessing spinal column integritycomprises receiving image data corresponding to a spinal column (step602). The receiving image data corresponding to a spinal column may beaccomplished in the same manner as or in a similar manner to step 202 ofthe method 600. For example, the image data may comprise or correspondto, for example, a three-dimensional image of a spinal column of apatient, and may comprise data corresponding to a plurality ofindividual cuts, slices, or sections of the spinal column of the patientthat together make up the three-dimensional image of the spinal column.Additionally or alternatively, the image data may comprise or correspondto one or more two-dimensional images of the spinal column of thepatient. For example, the preoperative image data may correspond toimages such as the image 300 of FIG. 3A, and/or the image 302 of FIG.3B. Where the image data comprises or corresponds to a plurality oftwo-dimensional images of the spinal column of the patient, theplurality of two-dimensional images may be sufficient to construct orreconstruct a three-dimensional image or model of the spinal column. Thepreoperative image data may correspond to a preoperative image taken ofthe spinal column of the patient using an imaging device 112, such as anMRI scanner, a CT scanner, or another imaging device. The preoperativeimage data may contain data for an entire spinal column of the patientor for a portion of the spinal column of the patient. The preoperativeimage data may be received from an imaging device 112, a database 114,the cloud 114, or any other source, and may be received via thecommunication interface 108.

The method 600 also comprises determining a material strength orstiffness of bony anatomy in at least a portion of the spinal column(step 604). The material strength or stiffness may be determined byprocessing the image data (e.g., in one or more of the ways describedabove with respect to the step 202 of the method 200), and/or byanalyzing metadata included with the image data. Where the image datacorresponds to a plurality of slices (which plurality of slices yields,for example and when taken together, a 3D image), the determining amaterial strength or stiffness of bony anatomy may comprise determininga material strength or stiffness of the bony anatomy in each of theplurality of slices. The material strength or stiffness of a givenportion of bony anatomy in the image data may be determined by measuringthe Hounsfield units of that portion of bony anatomy in the image data,and/or by completing a bone mineral density test or similar analysis. Insome embodiments, the image data may include data corresponding to animaged phantom having a known material strength or stiffness (or havingdifferent portions, each with a known material strength or stiffness),and the material strength or stiffness of a given portion of bonyanatomy may be determined by comparing a pixel intensity or othercharacteristic of the given portion of bony anatomy to the pixelintensity or other corresponding characteristic of the image datacorresponding to the phantom (or a portion thereof).

The method 600 further comprises completing a first stability assessmentof the spinal column (step 606). The stability assessment may becompleted in the same manner as or in a similar manner to the stabilityassessment completed in step 212 of the method 200. For example,assessing the stability of the spinal column may comprise running avirtual 6-degrees of motion analysis based on the image data to assess apreoperative stability of the spinal column. The analysis may evaluatethe stresses imposed on one or more vertebrae of the spinal columnrepresented in the image data while the spinal column is in a neutralposition and/or while the spinal column is in one or more positions offlexion. The result of a virtual 6-degrees of motion analysis may be acalculated level of stability of the spinal column; an indication of themaximum stress imposed on one or more vertebrae of the spinal column; anindication that a level of stress for one or more vertebrae of thespinal column exceeds a predetermined threshold (even prior to anydecompression procedure or other operation); or any other indicationrelating to the preoperative stability or instability of the spinalcolumn represented in the image data.

In some embodiments, the step 606 may comprise generating a virtualthree-dimensional model of the spinal column based on the image data.The various anatomical elements of the spinal column included in thevirtual three-dimensional model (or portions thereof) may be assigned amaterial strength or stiffness determined for that anatomical element(or portion thereof) in the step 604. The virtual 6-degrees of motionanalysis may then be conducted on and/or using the virtual 3D model,with the same result or results described above.

The method 600 also comprises modifying the image data to simulateremoval of bony anatomy and/or soft tissue from the spinal column (step608). The modifying may comprise identifying bony anatomy and/or softtissue to be removed from the spinal column to correct an identifiedstenosis or other condition of the spinal column, which may be done inthe same manner as or in a similar manner to that described above inconnection with the step 208 of the method 200. For example, in someembodiments, an algorithm may be used to identify bony anatomy and/orsoft tissue that needs to be removed to correct a stenotic condition.The bony anatomy may be, for example, a portion of one or morevertebrae, and the soft tissue may be, for example, all or part of adisc, a ligament, or other soft tissue.

In some embodiments, the identifying of bony anatomy and/or soft tissueto be removed may comprise receiving user input (e.g., from a surgeon orother user) via a user interface that identifies the bony anatomy and/orsoft tissue to be removed. For example, the user input may comprise auser selection of one of a laminotomy, laminectomy, foraminotomy,discectomy, or other procedure. Based on the user selection, arecommended portion of bony anatomy and/or soft tissue may beautomatically identified for removal, or the surgeon or other user mayidentify the portion of bony anatomy and/or soft tissue to be removed.For example, if the user input is a laminectomy, a lamina of a vertebraproximate the identified stenosis may be automatically identified forremoval, or the user may select a lamina of a vertebra for removal.

Often, the bony anatomy or soft tissue that needs to be removed in adecompression procedure is not readily accessible to a surgeon. As aresult, the surgeon must remove additional bony anatomy and/or softtissue from the spinal column simply to access the bony anatomy and/orsoft tissue causing the stenosis. In some embodiments, then,identification of the bony anatomy and/or soft tissue to be removedincludes not only identifying the bony anatomy and/or soft tissue thatneeds to be removed to correct the stenosis, but also the bony anatomyand/or soft tissue that needs to be removed to access thestenosis-causing bony anatomy and/or soft tissue. The bony anatomyand/or soft tissue that must be removed to access the stenosis-causingbony anatomy and/or soft tissue may be identified automatically (e.g.,by applying an algorithm selected based on the procedure to be completedand/or predefined by a machine learning engine or otherwise) or viaadditional user input.

The identifying step 208 may further comprise marking the identifiedbony anatomy and/or soft tissue in the image data (or, where a virtual3D model of the spinal column is being used, marking the identified bonyanatomy and/or soft issue in the virtual 3D model).

Once the bony anatomy and/or soft tissue to be removed has beenidentified, the modifying may occur in the same manner as or in asimilar manner to the step 210 of the method 200. For example, modifyingthe image data to simulate removal of the bony anatomy and/or softtissue may comprise substituting, in the image data, a virtual materialhaving little or no material strength or stiffness for the identifiedbony anatomy and/or soft tissue. Alternatively, modifying the image datato simulate removal of the bony anatomy and/or soft tissue may comprisesimply deleting the portion of the image data representing the bonyanatomy and/or the soft tissue to be removed. As another alternative,the virtual removal of the bony anatomy and/or soft tissue may compriseassigning a Hounsfield units measurement of zero to the portion of theimage data representing the bony anatomy and/or the soft tissue to beremoved.

In embodiments where a virtual 3D model is used for the first stabilityassessment in step 606, the step 608 may comprise modifying the virtual3D model (rather than the image data) to simulate removal of bonyanatomy and/or soft tissue from the spinal column. The simulated removalof bony anatomy and/or soft tissue from the spinal column in the virtual3D model may comprise simply deleting from the model that portion ofeach vertebra, disc, ligament, or other bony anatomy and/or soft tissuethat corresponds to the bony anatomy and/or soft tissue to be removed.

The method 600 further comprises completing a second stabilityassessment of the spinal column using the modified image data (step610). The second stability assessment may be completed in the samemanner as or in a similar manner to the first stability assessment instep 606, except that the second stability assessment is based on themodified image data (or, in embodiments, where a virtual 3D model isbeing used, based on the modified virtual 3D model). The types ofresults of the second stability assessment may also be the same as orsimilar to the types of results described above in connection with thefirst stability assessment.

The method 600 also comprises comparing the second stability assessmentto the first stability assessment or a predetermined threshold (step612). The comparison may comprise comparing a calculated preoperativelevel of stability of the spinal column to a calculated or predictedpostoperative level of stability of the spinal column; comparing anindication of the preoperative maximum stress imposed on one or morevertebrae of the spinal column to an indication of the postoperativemaximum stress predicted to be imposed on one or more vertebrae of thespinal column; or a comparison of any other indication relating to thepreoperative and postoperative stability of the spinal columnrepresented in the image data and/or in the virtual 3D model. Inembodiments where the results of the first stability assessment and thesecond stability assessment each comprise an indication that is based onone or more predetermined thresholds, the comparison may compriseevaluating whether the indication has remained the same (e.g., that nochange in stability of the spinal column is expected), has changed forthe better, or has changed for the worse. In other embodiments where theresults of the first stability assessment and the second stabilityassessment each comprise an indication that is based on one or morepredetermined thresholds, the step 612 may not be needed.

The method 600 further comprises causing the result of the comparison tobe displayed via a user interface (step 614). The result of thecomparison may be displayed via a user interface such as the userinterface 110. The result may be displayed as a number, a range, acolor-coded indication (e.g., green for a low risk of postoperativeiatrogenic instability, yellow for a medium risk of postoperativeiatrogenic instability, and red for a high risk of postoperativeiatrogenic instability), as text (e.g., indicating that thepostoperative risk of iatrogenic instability is high, medium, or low),in graphical form (e.g., as a meter showing a plurality of possibleresults, with an arrow or other marker indicating the actual result), inany other manner, and/or in any combination of any of the foregoingmanners. In embodiments where the second stability assessment comprisedcomparing a calculated or measured value to a predetermined threshold(e.g., to determine whether a postoperative risk of iatrogenicinstability is high, medium, or low), information corresponding to thatcomparison (such as, for example, the result of the comparison and/orthe information used to make the comparison) maybe displayed via theuser interface.

Turning now to FIG. 7, a method 700 of assessing spinal column integritycomprises receiving image data corresponding to a spinal column (step702). The receiving image data corresponding to a spinal column may beaccomplished in the same manner as or in a similar manner to step 602 ofthe method 600 and/or step 202 of the method 200. In some embodiments,the image data may comprise a virtual three-dimensional model of thespinal column that was generated based on actual images of the spinalcolumn of the patient. In other embodiments, the step 702 may comprisegenerating a virtual three-dimensional model of the spinal column basedon the image data.

The method 700 also comprises automatically analyzing the image data toidentify stenosis in the spinal column (step 704) or, in embodimentswhere a virtual 3D model of the spinal column is generated based on theimage data, automatically analyzing the virtual 3D model to identifystenosis in the spinal column. The stenosis may be identified in thecentral canal and/or in a lateral recess of the spinal columnrepresented by the image data. The automatically analyzing the imagedata to identify stenosis in the spinal column may be accomplished inthe same manner as or in a similar manner to in step 204 of the method200. For example, in some embodiments, the stenosis may be identified bycomparing image data corresponding to a first portion of a spinal columnto image data corresponding to a second portion of the spinal columnthat is different than the first portion of the spinal column. As a morespecific example, the stenosis may be identified by comparing a diameterof the spinal cord at a first level of the spinal column with a diameterof the spinal cord at one or more other levels of the spinal column,which one or more other levels may be adjacent the first level. A suddenor rapid change in the diameter of the spinal column between or amongadjacent or proximate levels may indicate stenosis.

In other embodiments, the stenosis may be identified by applying apredefined algorithm to the image data. The algorithm may be or havebeen generated, for example, by a machine learning engine based ontraining data.

The method 700 further comprises receiving mobility data correspondingto an initial mobility assessment of the spinal column (step 706). Themobility data may be received together with or separately from the imagedata received in the step 702. The mobility data may be based on amobility assessment completed independently of the image data, or amobility assessment based on the image data. The mobility data maycorrespond to a 6-degrees of motion assessment, and/or to an assessmentof a maximum flexion/extension of the spinal column in a lateral planeand/or an anterior-posterior plane and/or around a vertical axis.

The method 700 also comprises modifying the image data to simulateremoval of bony anatomy and/or soft tissue from the spinal column (step708). The modifying the image data may be accomplished in the samemanner as or in a similar manner to the step 608 of the method 600and/or step 210 of the method 200. The simulated removal of bony anatomyand/or of soft tissue from the spinal column may be based on thestenosis identified in step 704 and may involve removing the bonyanatomy and/or soft tissue that is causing the identified stenosis. Thesimulated removal of bony anatomy and/or soft tissue may further involveremoving bony anatomy and/or soft tissue to enable access to the bonyanatomy and/or soft tissue that is causing the identified stenosis.

In some embodiments, the simulating removal of bony anatomy and/or softtissue from the spinal column may be based on a user input (e.g., from asurgeon or other user) received via a user interface (such as the userinterface 110) that identifies the bony anatomy and/or soft tissue to beremoved. For example, the user input may comprise a user selection ofone of a laminotomy, laminectomy, foraminotomy, discectomy, or otherprocedure. Based on the user selection, a recommended portion of bonyanatomy and/or soft tissue may be automatically identified for removal,or the surgeon or other user may identify the portion of bony anatomyand/or soft tissue to be removed. For example, if the user input is alaminectomy, a lamina of a vertebra proximate the identified stenosismay be automatically identified for removal, or the user may select alamina of a vertebra for removal.

In some embodiments (e.g., where the image data does not comprise avirtual 3D model of the spinal column, and the step 702 does notcomprise generating such a virtual 3D model), the step 706 may comprisegenerating a virtual three-dimensional model of the spinal column basedon the image data. The various anatomical elements of the spinal columnincluded in the virtual three-dimensional model (or portions thereof)may be modified to simulate removal of bony anatomy and/or soft tissuefrom the spinal column.

The method 700 further comprises generating an updated mobilityassessment of the spinal column (step 710). The updated mobilityassessment may be completed based on the modified image data (ormodified virtual 3D model) resulting from the step 708. The updatedmobility assessment may correspond to a 6-degrees of motion assessment,and/or to an assessment of a maximum flexion/extension of the spinalcolumn in a lateral plane and/or an anterior-posterior plane and/oraround a vertical axis.

Depending on the results of the updated mobility assessment of thespinal column, which may be displayed or otherwise reported to a surgeonor other user, a determination may be made as to whether a predictedrisk of postoperative iatrogenic instability of the spinal column ishigh enough to justify planning and/or completing a spinal fusionprocedure or other procedure to improve the stability of the spinalcolumn. The method 700 thus beneficially facilitates an objectiveevaluation of whether a decompression or other spinal procedure willlikely result in iatrogenic instability, and whether a spinal fusion orother procedure to improve spinal stability is or is likely to be neededfollowing the initial procedure.

Although the foregoing disclosure has focused primarily on correctingcompression of a spinal cord, the systems and methods disclosed hereinmay be used to correct compression of a traversing or exiting nerve aswell. Moreover, the systems and methods disclosed herein may be used toassess a risk of iatrogenic instability in connection with any spinalprocedure, not just decompression procedures intended to correct astenotic condition.

As may be appreciated based on the foregoing disclosure, the presentdisclosure encompasses methods with fewer than all of the stepsidentified in FIGS. 2, 6, and 7 (and the corresponding description), aswell as methods that include steps from more than one of FIGS. 2, 6, and7 (and the corresponding description).

In some embodiments, one or more steps of any of the methods 200, 600,and/or 700 may be repeated one or more times, e.g., to allow a surgeonor other user to test the effect on predicted spinal integrity of aplurality of different procedures or implementations of a givenprocedure.

The foregoing discussion has been presented for purposes of illustrationand description. The foregoing is not intended to limit the disclosureto the form or forms disclosed herein. In the foregoing DetailedDescription, for example, various features of the disclosure are groupedtogether in one or more aspects, embodiments, and/or configurations forthe purpose of streamlining the disclosure. The features of the aspects,embodiments, and/or configurations of the disclosure may be combined inalternate aspects, embodiments, and/or configurations other than thosediscussed above. This method of disclosure is not to be interpreted asreflecting an intention that the claims require more features than areexpressly recited in each claim. Rather, as the following claimsreflect, inventive aspects lie in less than all features of a singleforegoing disclosed aspect, embodiment, and/or configuration. Thus, thefollowing claims are hereby incorporated into this Detailed Description,with each claim standing on its own as a separate preferred embodimentof the disclosure.

Moreover, though the description has included description of one or moreaspects, embodiments, and/or configurations and certain variations andmodifications, other variations, combinations, and modifications arewithin the scope of the disclosure, e.g., as may be within the skill andknowledge of those in the art, after understanding the presentdisclosure. It is intended to obtain rights which include alternativeaspects, embodiments, and/or configurations to the extent permitted,including alternate, interchangeable and/or equivalent structures,functions, ranges or steps to those claimed, whether or not suchalternate, interchangeable and/or equivalent structures, functions,ranges or steps are disclosed herein, and without intending to publiclydedicate any patentable subject matter.

What is claimed is:
 1. A system for assessing spinal stability,comprising: at least one processor; and a memory storing instructionsfor execution by the at least one processor that, when executed, causethe at least one processor to: receive image data corresponding to aspinal column of a patient; receive mobility data corresponding to aninitial mobility assessment of the spinal column, the initial mobilityassessment being indicative of a preoperative stability of the spinalcolumn; modify the image data to simulate removal of bony anatomy orsoft tissue from the spinal column to yield modified image data; andgenerate an updated mobility assessment of the spinal column based onthe modified image data, the updated mobility assessment beingindicative of a predicted postoperative stability of the spinal column.2. The system of claim 1, wherein the memory stores additionalinstructions that, when executed, further cause the at least oneprocessor to: analyze the image data to identify stenosis in the spinalcolumn.
 3. The system of claim 2, wherein the analysis is based on acomparison of image data corresponding to a first portion of the spinalcolumn to image data corresponding to a second portion of the spinalcolumn, the second portion being different than the first portion. 4.The system of claim 2, wherein the analysis utilizes a predefinedalgorithm.
 5. The system of claim 2, wherein the at least one processormodifies the image data to simulate removal of the bony anatomy or thesoft tissue based on the identified stenosis.
 6. The system of claim 1,wherein the memory stores additional instructions that, when executed,further cause the at least one processor to: modify the image data tosimulate removal of the bony anatomy or the soft tissue in response toreceiving user input that corresponds to a user selection of one of alaminectomy, a laminotomy, or a foraminotomy.
 7. The system of claim 1,wherein the simulated removal of the bony anatomy or the soft tissuecorresponds both to removal of first bony anatomy or first soft tissuefrom the spinal column to correct stenosis, and to removal of secondbony anatomy or second soft tissue from the spinal column to enableaccess to the first bony anatomy or the first soft tissue.
 8. The systemof claim 1, wherein the mobility data is based on the image data.
 9. Thesystem of claim 1, wherein the mobility data is independent of the imagedata.
 10. A system for assessing spinal stability, comprising: at leastone processor; and a memory storing instructions for execution by the atleast one processor that, when executed, cause the at least oneprocessor to: receive preoperative image data of a spine of a patient;identify spinal stenosis at one or more levels of the spine based on thepreoperative image data; simulate removal of anatomy at the one or morelevels of the spine based on a predicted correction for the spinalstenosis to generate modified image data; and generate a predictedpostoperative stability assessment of the spine based on the modifiedimage data.
 11. The system of claim 10, wherein the memory storesadditional instructions that, when executed, further cause the at leastone processor to: generate a surgical plan to correct the spinalstenosis at the one or more levels of the spine based on the predictedpostoperative stability assessment; and render, to a user interface, thesurgical plan.
 12. The system of claim 11, wherein the surgical plancomprises a decompression plan for carrying out the removal of theanatomy at the one or more levels of the spine.
 13. The system of claim11, wherein the surgical plan comprises a fusion plan for implanting anobject into the patient.
 14. The system of claim 13, wherein the objectcomprises a bone graft or an implant for spinal fusion.
 15. The systemof claim 11, wherein the memory stores additional instructions that,when executed, further cause the at least one processor to: receivepostoperative image data of the spine generated after carrying out thesurgical plan to correct the spinal stenosis at the one or more levelsof the spine; and generate a postoperative stability assessment of thespine based on the postoperative image data.
 16. The system of claim 15,wherein the memory stores additional instructions that, when executed,further cause the at least one processor to: generate an updatedsurgical plan based on the postoperative stability assessment of thespine; and render, to the user interface, the updated surgical plan. 17.The system of claim 10, wherein the at least one processor identifiesthe spinal stenosis by comparing one or more attributes of the spine ata first location to one or more attributes of the spine at a secondlocation.
 18. The system of claim 10, wherein the anatomy corresponds toat least one of bony anatomy and soft tissue.
 19. A system for assessingspinal stability, comprising: at least one processor; and a memorystoring instructions for execution by the at least one processor that,when executed, cause the at least one processor to: identify spinalstenosis at one or more levels of a spine of patient based onpreoperative image data of the spine; generate a predicted postoperativestability assessment of the spine based on modified image data thatsimulates removal of anatomy from the preoperative image data; generatea surgical plan to correct the spinal stenosis at the one or more levelsof the spine based on the predicted postoperative stability assessment;and generate a postoperative stability assessment of the spine based onpostoperative image data of the spine generated subsequent to carryingout the surgical plan.
 20. The system of claim 19, wherein the memorystores additional instructions that, when executed, further cause the atleast one processor to: generate an updated surgical plan when thepostoperative stability assessment indicates that integrity of the spineis compromised; and render, to a user interface, the updated surgicalplan.